Transparent and comparable evaluation of ecological restoration outcomes is essential for advancing performance-based environmental governance and ESG-aligned ecological compensation. However, existing grassland monitoring approaches in semi-arid regions often rely on single vegetation indices, which fail to capture ecosystem structure, functional recovery, and temporal dynamics. To address these limitations, this study proposes a process-oriented Restoration Index (RI) based on multi-source remote sensing data. By integrating spectral, textural, and phenological indicators, together with topographic and climatic factors, derived from Sentinel-2 and Landsat time-series imagery, the framework characterizes vegetation productivity, community structure, and seasonal ecological processes within a unified analytical framework. A case study in the Xilingol grassland of Inner Mongolia shows that different management strategies, including grazing exclusion, reseeding, and rotational grazing, are associated with distinct restoration trajectories and recovery performance. The results indicate that the RI captures both spatial heterogeneity and temporal evolution of ecosystem recovery, while the normalization procedure improves the relative comparability of restoration assessment results within the adopted framework. Quantitative evaluation shows positive agreement with field observations, providing preliminary support for the applicability of the approach within the study area. Overall, the RI framework provides a scalable and policy-relevant basis for ecological restoration assessment and may support ecological compensation evaluation, environmental auditing, and more transparent restoration governance.
Gao et al. (Tue,) studied this question.